Seismic organic facies constrained geostatistical inversion for petropyhsical properties in shale reservoirs

Shale oil and gas represent a critical component of the transitional energy mix; improved geophysical characterization of high-quality reservoirs can contribute to reduced greenhouse gas emissions during the ongoing expansion of renewable energy infrastructure. This research aims to treat the geochemical parameters—thermal maturity and total organic carbon (TOC) content—as a seismic organic facies, and incorporate this concept into the geophysical inversion workflow. First, we compute the maturity levels from well logs and classify them into three seismic facies that capture the primary pattern of hydrocarbon potential. Then, we estimate the distributions of petrophysical parameters (clay volume, TOC content, and porosity) conditioned by facies. Next, we propose a probability field simulation based on Gaussian mixture model (GMM-PFS) algorithm to generate a high-resolution ensemble of prior models of petrophysical properties. Finally, we employ the ES-MDA inversion algorithm to integrate the rock physics model with seismic forward modeling into a single workflow for estimating the reservoir properties of organic-rich shales. The results show that the proposed method has better consistency with the verification well, particularly in terms of the reservoir locations. The synthetic tests demonstrate that, compared to the Bayesian rock physics inversion method, the proposed approach achieves higher accuracy and better robustness. Results from both 2D and 3D field data further confirm that the method provides better agreement with validation wells and effectively captures the spatial heterogeneity of reservoirs. We conclude that this method is a promising tool for reducing the uncertainty in shale reservoir characterization and improving the understanding of subsurface properties, thereby contributing indirectly to lower CO2 emissions during hydrocarbon production.